Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning

Feng Yan

Abstract:

Data heterogeneity has been identified as one of the key features
in federated learning but often overlooked in the lens
of robustness to adversarial attacks. This paper focuses on
characterizing and understanding its impact on backdooring
attacks in federated learning through comprehensive experiments
using synthetic and the LEAF benchmarks. The initial
impression driven by our experimental results suggests that
data heterogeneity is the dominant factor in the effectiveness
of attacks and it may be a redemption for defending against
backdooring as it makes the attack less efficient, more challenging
to design effective attack strategies, and the attack
result also becomes less predictable. However, with further
investigations, we found data heterogeneity is more of a curse
than a redemption as the attack effectiveness can be significantly
boosted by simply adjusting the client-side backdooring
timing. More importantly, data heterogeneity may result
in overfitting at the local training of benign clients, which
can be utilized by attackers to disguise themselves and fool
skewed-feature based defenses. In addition, effective attack
strategies can be made by adjusting attack data distribution.
Finally, we discuss the potential directions of defending the
curses brought by data heterogeneity. The results and lessons
learned from our extensive experiments and analysis offer
new insights for designing robust federated learning methods
and systems.